Multiplayer video game matchmaking system and methods

ABSTRACT

Embodiments of systems presented herein may identify users to play a multiplayer video game together using a mapping system and machine learning algorithms to create sets of matchmaking plans for the multiplayer video game that increases player or user retention. Embodiments of systems presented herein can determine the predicted churn rate, or conversely retention rate, of a user waiting to play a video game if the user is matched with one or more additional users in a multiplayer instance of the video game.

INCORPORATION BY REFERENCE TO ANY PRIORITY APPLICATIONS

This disclosure claims priority to U.S. Provisional Application No.62/411,452, which was filed on Oct. 21, 2016 and is titled “EngagementOptimized Matchmaking Framework,” the disclosure of which is herebyexpressly incorporated by reference herein in its entirety for allpurposes. Any and all applications for which a foreign or domesticpriority claim is identified in the Application Data Sheet as filed withthe present application are hereby incorporated by reference under 37CFR 1.57.

BACKGROUND

Software developers typically desire for their software to engage usersfor as long as possible. The longer a user is engaged with the software,the more likely that the software will be successful. The relationshipbetween the length of engagement of the user and the success of thesoftware is particularly true with respect to video games. The longer auser plays a particular video game, the more likely that the user enjoysthe game and thus, the more likely the user will continue to play thegame.

The principle of engagement is not limited to single player games andcan also be applied to multiplayer video games. Video games that provideusers with enjoyable multiplayer experiences are more likely to haveusers play them again. Conversely, video games that provide users withpoor multiplayer experiences are less likely to maintain a high numberof users. Thus, one of the challenges of video game development is toprovide a mechanism that ensures or increases the probability of anenjoyable multiplayer experience.

SUMMARY OF EMBODIMENTS

The systems, methods and devices of this disclosure each have severalinnovative aspects, no single one of which is solely responsible for theall of the desirable attributes disclosed herein. Details of one or moreimplementations of the subject matter described in this specificationare set forth in the accompanying drawings and the description below.

Certain embodiments of the present disclosure relate to acomputer-implemented method that may be implemented by an interactivecomputing system configured with specific computer-executableinstructions. The method may include selecting a plurality of users froma pool of users. The pool of users may include users available forselection to play an instance of a video game. At least a first portionof the instance of the video game may execute on a user computing deviceof at least one user from the plurality of users and a second portion ofthe instance of the video game may execute on the interactive computingsystem. Further, the method may include creating a connected graphcomprising a plurality of vertexes and a plurality of edges. Each vertexmay be connected by at least one edge from the plurality of edges andeach vertex may represent a different user from the plurality of users.The method may include accessing user interaction data for each user ofthe plurality of users. The user interaction data may correspond to theuser's interaction with the video game. For each edge in the connectedgraph, the method may include assigning a weight to the edge based atleast in part on a first churn risk of a first user corresponding to afirst node of the edge and a second churn risk of a second usercorresponding to a second node of the edge. The first churn risk may bebased at least in part on the user interaction data for the first userand the second churn risk may be based at least in part on the userinteraction data for the second user. Moreover, the method may includeselecting a set of edges from the connected graph to obtain a set ofselected edges based at least in part on the weights assigned to eachedge within the connected graph. Each vertex in the connected graph maybe connected to at least one edge within the set of selected edges.Further, the method may include initiating a playable instance of thevideo game using at least a pair of users corresponding to vertexes ofone or more edges included in the set of selected edges.

Other embodiments of the present disclosure relate to a system that mayinclude an electronic data store configured to store user interactiondata for users of a video game and a hardware processor in communicationwith the electronic data store. The hardware processor may be configuredto execute specific computer-executable instructions to at least selecta plurality of users available to play an instance of a video game. Atleast a first portion of the instance of the video game may execute on auser computing device of at least one user from the plurality of usersand a second portion of the instance of the video game may execute onthe interactive computing system. Further, the hardware processor may beconfigured to execute specific computer-executable instructions to atleast create a connected graph with each vertex representing a differentuser from the plurality of users and to access user interaction datafrom the electronic data store for each user of the plurality of users.The user interaction data may correspond to the user's interaction withthe video game. For each edge in the connected graph, the hardwareprocessor may be configured to execute specific computer-executableinstructions to at least assign a weight to the edge based at least inpart on a first churn risk of a first user corresponding to a first nodeof the edge and a second churn risk of a second user corresponding to asecond node of the edge. The first churn risk may be based at least inpart on the user interaction data for the first user and the secondchurn risk may be based at least in part on the user interaction datafor the second user. Moreover, the hardware processor may be configuredto execute specific computer-executable instructions to at least selecta set of edges from the connected graph based at least in part on theweights assigned to each edge within the connected graph. Each vertex inthe connected graph may be connected to at least one edge within the setof selected edges. In addition, the hardware processor may be configuredto execute specific computer-executable instructions to at leastinitiate a playable instance of the video game using at least two userscorresponding to vertexes of one or more edges included in the set ofselected edges.

Yet, other embodiments of the present disclosure relate to anon-transitory computer-readable storage medium storing computerexecutable instructions that, when executed by one or more computingdevices, may configure the one or more computing devices to performoperations comprising selecting a plurality of users from a pool ofusers who are available to play an instance of a video game. At least afirst portion of the instance of the video game may execute on a usercomputing device of at least one user from the plurality of users and asecond portion of the instance of the video game may execute on theinteractive computing system. Further, the operations may includecreating a connected graph with each vertex representing a differentuser from the plurality of users and accessing user interaction data foreach user of the plurality of users. The user interaction data maycorrespond to the user's interaction with the video game. For each edgein the connected graph, the operations may include assigning a weight tothe edge based at least in part on a first churn risk of a first usercorresponding to a first node of the edge and a second churn risk of asecond user corresponding to a second node of the edge. The first churnrisk may be based at least in part on the user interaction data for thefirst user and the second churn risk may be based at least in part onthe user interaction data for the second user. Moreover, the operationsmay include selecting a set of edges from the connected graph based atleast in part on the weights assigned to each edge within the connectedgraph. Each vertex in the connected graph may be connected to one ormore edges within the set of selected edges. Further, the operations mayinclude initiating a playable instance of the video game using aplurality of users corresponding to vertexes of one or more edgesincluded in the set of selected edges.

Although certain embodiments and examples are disclosed herein,inventive subject matter extends beyond the examples in the specificallydisclosed embodiments to other alternative embodiments and/or uses, andto modifications and equivalents thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

Throughout the drawings, reference numbers are re-used to indicatecorrespondence between referenced elements. The drawings are provided toillustrate embodiments of the subject matter described herein and not tolimit the scope thereof.

FIG. 1A illustrates an embodiment of a networked computing environmentthat can implement one or more embodiments of a dynamic user matchmakingsystem for a video game.

FIG. 1B illustrates an embodiment of a model generation system of FIG.1A.

FIG. 1C illustrates an embodiment of a retention analysis system of FIG.1A.

FIG. 2 presents a flowchart of an embodiment of a prediction modelgeneration process.

FIG. 3 presents an example of an embodiment of a connected graph ofusers.

FIG. 4 presents a flowchart of an embodiment of a multiplayer matchingprocess.

FIG. 5 illustrates an embodiment of a user computing system.

FIG. 6 illustrates an embodiment of a hardware configuration for theuser computing system of FIG. 5.

DETAILED DESCRIPTION OF EMBODIMENTS

Introduction

It is generally desirable for a video game to appeal to a large numberof users. This principle is also true for multiplayer video games.Multiplayer video games can include video games where two or more usersplay against each other, video games where two or more users play on thesame team, and/or video games where teams of multiple users play againsteach other.

Multiplayer games with poor matchmaking algorithms can result in lowerengagement by users. In other words, poorly matched opponents and/orteammates may result in users ceasing to play a video game or playingthe video game less often than if the multiplayer game has bettermatchmaking algorithms. Poor matchmaking may include, among otherthings, matching users of incompatible or inadequately paired skilllevels or contradicting play style preferences. For some users, beingpaired with a user of a different skill level or a different play stylemay be undesirable and may be considered poor matchmaking. But, forother users, being paired with a user of a different skill level or adifferent play style may be desirable. Thus, determining whethermatchmaking is poor or is good can depend on the specific users analyzedby the matchmaking algorithms.

Embodiments presented herein use a graph mapping system and machinelearning algorithms to identify sets of matchmaking plans for amultiplayer video game that optimizes or increases player or userretention. Systems presented herein can determine the predicted churnrate, or conversely retention rate, of a user waiting to play a videogame for different matchups of the user with one or more additionalusers in a multiplayer instance of the video game. Further, embodimentsof the systems herein can generate a graph topology of the users andperform one or more edge selection algorithms based at least in part onthe determined churn, or retention, rates to optimize or increase thequality of player matchmaking with respect to retention or churn rate.Example embodiments of systems and algorithms that can be used todetermine a retention rate and for generating a match plan using theretention rate are described in U.S. application Ser. No. 15/064,115,filed on Mar. 8, 2016 and titled “MULTIPLAYER VIDEO GAME MATCHMAKINGOPTIMIZATION,” which is hereby incorporated by reference in its entiretyand for all purposes herein.

To simplify discussion, this disclosure primarily focuses on increasinguser retention or reducing the churn rate of users playing themultiplayer video game. However, the objective is not limited as such,and embodiments of the present disclosure can be used to optimize orincrease the likelihood of one or more additional or alternativeobjectives. For example, the objectives may include one or more of:increasing in-game spending of virtual or real currency; decreasingundesirable behavior (for example, foul language or harassment of otherusers) within the video game, or reducing the occurrence of usersprematurely exiting an instance of the video game.

To simplify discussion, the present disclosure is primarily describedwith respect to a video game. However, the present disclosure is notlimited as such and may be applied to other types of applications. Forexample, embodiments disclosed herein may be applied to educationalapplications (for example, applications that help users learn a newlanguage) or other applications that may pair together two or more usersin a group.

Example Networked Computing Environment

FIG. 1A illustrates an embodiment of a networked computing environment100 that can implement one or more embodiments of a dynamic usermatchmaking system for a video game 112. The networked computingenvironment 100 includes a plurality of user computing systems 110 thatcan communicate with an interactive computing system 130 via a network104.

At least some of the user computing systems 110 may include, host, orexecute a video game 112. In some embodiments, user computing system 110can host or execute a portion of the video game 112 and the applicationhost system 138 can host and/or execute a portion of the video game 112.When a user initiates execution of the video game 112 on the usercomputing system 110, a network connection may be established with theinteractive computing system 130 and the two portions of the video game112 may execute in conjunction with each other. For example, theapplication host system 138 may host and execute a portion of the videogame 112 that comprises a video game environment while the usercomputing system 110 may execute a portion of the video game 112 thatenables a user to interact with the video game environment using, forexample, a playable in-game character. The video game environment mayinclude an online or digital persistent world that may be maintainedafter a user of the user computing system 110 disconnects from theapplication host system 138. As another example, the video game may be amassively multiplayer online role-playing game (MMORPG) that includes aclient portion executed by the user computing system 110 and a serverportion executed by one or more application host systems (not shown)that may be included as part of the interactive computing system 130.

As previously mentioned, the application host system 138 may host and/orexecute at least a portion of the video game 112. Alternatively, or inaddition, the application host system 138 may host or execute theentirety of the video game 112 and a user may interact with the videogame 112 using the user computing system 110.

The user computing system 110 may include hardware and softwarecomponents for establishing communication with another computing system,such as the interactive computing system 130, over a communicationnetwork 104. For example, the user computing system 110 may be equippedwith networking equipment and network software applications (forexample, a web browser) that facilitate communications via a network(for example, the Internet) or an intranet. The user computing system110 may include a number of local computing resources, such as centralprocessing units and architectures, memory, mass storage, graphicsprocessing units, communication network availability and bandwidth, andso forth. Further, the user computing system 110 may include any type ofcomputing system. For example, the user computing system 110 may includeany type of computing device(s), such as desktops, laptops, video gameplatforms, television set-top boxes, televisions (for example, InternetTVs), network-enabled kiosks, car-console devices, computerizedappliances, wearable devices (for example, smart watches and glasseswith computing functionality), and wireless mobile devices (for example,smart phones, PDAs, tablets, or the like), to name a few. In someembodiments, the user computing system 110 may include one or more ofthe embodiments described below with respect to FIG. 5 and FIG. 6.

As previously discussed, it may be desirable to maintain or increase auser's level of engagement with the video game 112. One solution formaintaining or increasing the user's level of engagement with the videogame 112 includes matching a user with other users who have playcharacteristics, or application interactivity characteristics, that theuser desires or tends to prefer in opponents and/or teammates. Theseplay characteristics may include characteristics relating to skilllevel, play style (for example, a user who plays defensively, playsoffensively, plays a support role, prefers stealth attacks, prefers touse magic abilities, or prefers to use melee abilities, and the like),and/or sportsmanship (for example, a user who is a gracious winner orloser, is or is not gregarious, or does not insult other users, and thelike). It should be noted that although the term play characteristics isused, the play characteristics are not necessarily limited tocharacteristics related to playing the video game 112. In someembodiments, the play characteristics may include one or more additionalor alternative characteristics relating to matching users for amultiplayer game. These additional or alternative characteristics mayinclude characteristics that relate to improving the play experience ofusers in a multiplayer game. For example, the additional or alternativecharacteristics may include user geographic location, user locationwithin a network, characteristics of a user computing system 110 of auser, and network characteristics of a portion of a network (such as thelast mile, or the home network of the user) in communication with theuser computing system 110 of a user, and the like.

The play characteristics may be determined based at least in part onuser interaction data for one or more users and/or express requests orindications from the one or more users. Further, the playcharacteristics that a user desires or tends to prefer in opponentsand/or teammates may be determined based at least in part on userinteraction data for the user.

Interactive computing system 130 may include a number of systems orsubsystems for facilitating the determination of the playcharacteristics of a particular user of the video game 112 and/or thedesired play characteristics of opponent and/or teammate users for theparticular user of the video game 112. Further, the interactivecomputing system 130 may include a number of systems and subsystems forfacilitating matchmaking of users of the video game 112 based at leastin part on the play characteristics of the users. These systems orsubsystems can include a user graph generator 120, a user matchingsystem 132, a skill evaluation system 136, an application host system138, a user data repository 142, a retention analysis system 140, and amodel generation system 146. Each of these systems may be implemented inhardware, and software, or a combination of hardware and software.Further, each of these systems may be implemented in a single computingsystem comprising computer hardware or in one or more separate ordistributed computing systems. Moreover, while these systems are shownin FIG. 1A to be stored or executed on the interactive computing system130, it is recognized that in some embodiments, part or all of thesesystems can be stored and executed on the user computing system 110.

The user graph generator 120 generates a graph of a set of users thatare waiting to play an instance of a video game. The set of users mayinclude all users waiting to play the instance of the video game or asubset of users that may be selected using a selection process, such asfirst come-first served or threshold number of users matching a skilllevel range. The users may be represented by nodes or vertices in thegraph and potential matchups, as either teammates or opponents, may berepresented by edges between the nodes or vertices. Further, the usergraph generator 120 may weight edges based at least in part on one ormore selected objectives. For example, the edges may be weighted basedon a churn rate for the users connected by an edge in the graph. It isrecognized that other data structures may also be used to represent thedata stored in the graph, such as, for example, an adjacency matrix, amatrix, an edge list, an adjacency list, a node listing, and so forth.

The user matching system 132 identifies or matches two or more userstogether for playing the video game 112. The two or more users may bematched as opponents, teammates, or a combination of opponents andteammates. To identify users to be matched as opponents or teammates,the user matching system 132 may access a user graph generated by theuser graph generator 120. Further, the user matching system 132 maysolve a minimum or maximum weight matching problem on the graphgenerated by the user graph generator 120 to identify a pairing or matchplan of users to play an instance of the video game 112.

The model generation system 146 can use one or more machine learningalgorithms to generate one or more prediction models or parameterfunctions. One or more of these parameter functions may be used todetermine an expected value or occurrence based on a set of inputs. Forexample, a prediction model can be used to determine an expected churnrate or a probability that a user will cease playing the video game 112based on one or more inputs to the prediction model, such as, forexample, historical user interaction information for a user. As anotherexample, a prediction model can be used to determine an expected amountof money spent by the user on purchasing in-game items for the videogame based on one or more inputs to the prediction model. In some cases,the prediction model may be termed a prediction model because, forexample, the output may be or may be related to a prediction of anaction or event, such as the prediction the user continues to play thevideo game 112. A number of different types of algorithms may be used bythe model generation system 146. For example, certain embodiments hereinmay use a logistical regression model. However, other models arepossible, such as a linear regression model, a discrete choice model, ora generalized linear model.

The machine learning algorithms can be configured to adaptively developand update the models over time based on new input received by the modelgeneration system 146. For example, the models can be regenerated on aperiodic basis as new user information is available to help keep thepredictions in the model more accurate as the user information evolvesover time. The model generation system 146 is described in more detailherein. After a model is generated, it can be provided to the retentionanalysis system 140.

Some non-limiting examples of machine learning algorithms that can beused to generate and update the parameter functions or prediction modelscan include supervised and non-supervised machine learning algorithms,including regression algorithms (such as, for example, Ordinary LeastSquares Regression), instance-based algorithms (such as, for example,Learning Vector Quantization), decision tree algorithms (such as, forexample, classification and regression trees), Bayesian algorithms (suchas, for example, Naive Bayes), clustering algorithms (such as, forexample, k-means clustering), association rule learning algorithms (suchas, for example, Apriori algorithms), artificial neural networkalgorithms (such as, for example, Perceptron), deep learning algorithms(such as, for example, Deep Boltzmann Machine), dimensionality reductionalgorithms (such as, for example, Principal Component Analysis),ensemble algorithms (such as, for example, Stacked Generalization),and/or other machine learning algorithms.

The retention analysis system 140 can include one or more systems fordetermining a predicted churn or retention rate for a user based on theapplication of user interaction data for the user and user interactiondata for additional users included in a match plan to a predictionmodel, such as a prediction model generated by the model generationsystem 140. In some cases, the user matching system 132 may use thepredicted retention rate determined by the retention analysis system 140to determine whether to select the match plan, or to generate a newmatch plan for playing an instance of the video game 112. For example,the user matching system 132 can solve a minimum weighting problem basedon the application of the predicted retention rates to edges of a usergraph created by the user graph generator 120. In some cases,determining whether to select the match plan includes matching theretention rate to data in a repository (not shown) that indicateswhether or not to select a new match plan.

Further, generation and application of the parameter functions orprediction models and their use in creating a match plan or determiningwhether to select a match plan for playing an instance of the video game112 will be described in further detail below with respect to theretention analysis system 140. In certain embodiments, the user matchingsystem 132 may be or may include the model generation system 146.Moreover, in some cases, the user matching system 132 may be or mayinclude the retention analysis system 140 or the user graph generator120.

In some embodiments, the retention analysis system 140 may receive aparticular match plan and user interaction data, features, and/ormetadata associated with each user included in the match plan. Theretention analysis system 140 can then output a retention rate and/or apredicted spending amount for each individual user included in the matchplan and/or for the match plan itself. For example, the retentionanalysis system 140 may receive a match plan that includes users A1, A2,and A3 on one team and users B1, B2, and B3 on another team. Using userinteraction data for each of the six users, the retention analysissystem 140 can determine a retention rate for each user, a retentionrate for the match plan, a predicted amount of spending for each user,and/or a predicted amount of spending in total for the match plan.

The skill evaluation system 136 can evaluate a skill level of the useraccessing or playing the video game 112. The skill level of the user maybe determined based at least in part on user interaction data related tothe user's access of the video game 112. In some cases, the skillevaluation system 136 may determine a general skill for the user playingthe video game 112. In other cases, the skill evaluation system 136 mayevaluate a plurality of different skills associated with playing thevideo game 112. These skills may vary based on the type of video game.For example, in a sports game, the skills may relate to the ability ofthe user to select the right play, the best play, or best play strategywhen playing the video game 112. As a second example, in a first-personshooter game, the skills may relate to the accuracy of the user inshooting at an enemy within the video game 112. The skill levelinformation may be associated with the user at the user data repository142.

The user data repository 142 can store user interaction informationassociated with one or more users' interaction with the video game 112and/or one or more other video games. This user interaction informationor data may include any type of information that can be used todetermine a user's play characteristics (such as skill level) and auser's desired play characteristics for opponents and/or teammates.Further, the user interaction information may be used to determine theuser's level of engagement with the video game 112 when playing with oragainst users associated with various play characteristics. For example,some non-limiting examples of the user interaction information mayinclude information relating to actions taken by the user within thevideo game 112; the level of success of the user; a measure of theuser's progress within the video game 112; whether the user wassuccessful at performing specific actions within the video game 112 orcompleting particular objectives within the video game 112; how long ittook the user to complete the particular objectives; how many attemptsit took the user to complete the particular objectives; how much moneythe user spent with respect to the video game 112, which may include oneor both of the amount of money spent to obtain access to the video game112 and the amount of money spent with respect to the video game 112exclusive of money spent to obtain access to the video game 112; howfrequently the user accesses the video game 112; how long the user playsthe video game 112; whether the user continues playing during a playsession after a defeat or failure to satisfy an objective, or the like.Each of these types of user interaction information may be collected,divided, weighted, and/or characterized based at least in part on playcharacteristics of other users that play with or against the user. Forexample, the user interaction information relating to the level ofsuccess of the user when playing the video game 112 may be weighted orcategorized differently based on play characteristics of teammates. Forinstance, the level of success of the user may be weighted higher orlower based on whether the play characteristics of a teammate indicatethat the teammate has a lower or higher skill level than the user.

Generally, the user interaction information may be monitored and/orobtained by systems of the interactive computing system 130. However, insome cases, the user computing system 110 may monitor and/or obtain atleast some of the user interaction information. In such cases, the usercomputing system 110 may share the user interaction information with theinteractive computing system 130 via the network 104. In someembodiments, some or all of the user interaction information is notstored by the video game 112, but is instead provided to or determinedby another portion of the user computing system 110 external to thevideo game 112 and/or by the interactive computing system 130. Each ofthe repositories described herein may include non-volatile memory or acombination of volatile and nonvolatile memory.

The network 104 can include any type of communication network. Forexample, the network 104 can include one or more of a wide area network(WAN), a local area network (LAN), a cellular network, an ad hocnetwork, a satellite network, a wired network, a wireless network, andso forth. Further, in some cases, the network 104 can include theInternet.

Example Model Generation System

FIG. 1B illustrates an embodiment of the model generation system 146 ofFIG. 1A. The model generation system 146 may be used to generate ordetermine one or more prediction models 160 based on historical data 152for a number of users. Typically, although not necessarily, thehistorical data 152 includes data associated with a large number ofusers, such as hundreds, thousands, hundreds of thousands, or more,users. However, the present disclosure is not limited as such, and thenumber of users may include any number of users. Further, the historicaldata 152 can include data received from one or more data sources, suchas, for example, an application host system (not shown) and/or one ormore user computing systems 112. Moreover, the historical data 152 caninclude data from different data sources, different data types, and anydata generated by one or more user's interaction with the video game112. In some embodiments, the historical data 152 may include a verylarge number of data points, such as millions of data points, which maybe aggregated into one or more data sets. In some cases, the historicaldata 152 may be accessed from a user data repository 142. In someembodiments, the historical data 152 is limited to historicalinformation about the video game, but in other embodiments, thehistorical data 152 may include information from one or more other videogames. Further, in some embodiments, one or more subsets of thehistorical data are limited by a date restriction, such as limited toinclude data from the last 6, 9, or 12 months.

The historical data 152 may include user interaction data for the userswith respect to the video game 112. Further, the historical data 152 mayinclude information relating to opponents and/or teammates of the users.

The model generation system 146 may, in some cases, also receivefeedback data 154. This data may be received as part of a supervisedmodel generation process that enables a system user, such as anadministrator, to provide additional input to the model generationsystem 146 that may be used to facilitate generation of the predictionmodel 160. For example, if an anomaly exists in the historical data 152,the system user may tag the anomalous data enabling the model generationsystem 146 to handle the tagged data differently, such as applying adifferent weight to the data or excluding the data from the modelgeneration process.

Further, the model generation system 146 may receive control data 156.This control data 156 may identify one or more features orcharacteristics for which the model generation system 146 is todetermine a model. Further, in some cases, the control data 156 mayindicate a value for the one or more features identified in the controldata 156. For example, suppose the control data 156 indicates that aprediction model is to be generated using the historical data 152 todetermine a length of time that the users played the video game 112. Ifthe amount of time each user played the game is known, this data may beprovided as part of the control data 156, or as part of the historicaldata 152. As another example, if the prediction model is to be generatedto estimate a retention rate as determined, for example, based onwhether the users played the video game 112 for a threshold period oftime or continue to play the video game 112 after a particular thresholdperiod of time, the control data 156 may include the retention rate,opponent data, and/or teammate data for the users whose data is includedin the historical data 152.

The model generation system 146 may generally include a model generationrule set 170 for generation of the prediction model 160. The rule set170 may include one or more parameters 162. Each set of parameters 162may be combined using one or more mathematical functions to obtain aparameter function. Further, one or more specific parameters may beweighted by the weights 164. In some cases, the parameter function maybe obtained by combining a set of parameters with a respective set ofweights 164. The prediction model 160 and/or the respective parameters162 of the prediction models 160 may be derived during a trainingprocess based on particular input data, such as the historical data 152,feedback data 154, and control data 156, as well as defined outputcriteria, which may be included with the control data 156, used fortraining purposes. The model generation rule set 170 can define thespecific machine learning rules and/or algorithms the model generationsystem 146 uses to generate the model based on a defined objectivefunction, such as determining a churn rate. In some embodiments, initialparameters 162 and weights 164 can be manually provided during theinitiation of the model generation process. The parameters 162 andweights 164 can be updated and modified during the model generationphase to generate the prediction model 160.

The model generation system 146 can filter and categorize the historicaldata sets according to various characteristics and parameters of thedata. For example, the data can be categorized by the data source (suchas, for example, game application data, host application data, or userprofile data), information type (such as, for example, gameplayinformation, transaction information, interaction information, or gameaccount information), opponent data (such as, for example, skill ofopponent, role selected or played by opponent, or success rate verseopponent), teammate data (such as, for example, skill of teammates,roles selected or played by teammates, or success rate when playing witha particular teammate), or other categories associated with the data.The model generation system 146 can filter the information to identifythe information for further processing. In some embodiments, the modelgeneration system 146 is configured to filter and separate thehistorical data 152 into a plurality of data types or categories beforefurther processing. Moreover, in some cases, some of the historical data152 may be filtered out or removed from the historical data 152 based onthe data being associated with a relevance that does not satisfy athreshold relevance as determined by the model generation system 146.

Optionally, one or more of the prediction models 160 may be associatedwith a penalty 166. These penalties 166 may be used to facilitate thegeneration of or selection of a particular prediction model 160 based onone or more factors that are used to derive the penalty. For example,the mathematical complexity or the number of parameters included in aparticular prediction model 160 may be used to generate a penalty forthe particular prediction model 160, which may impact the generation ofthe model and/or a selection algorithm or a selection probability thatthe particular prediction model 160 is selected.

After the prediction model 160 has been generated, the model can be usedduring runtime of the retention analysis system 140 and/or the usermatching system 132 to approve, reject, or select a match plan forplaying an instance of the video game 112. In some cases, the predictionmodel 160 may be used to facilitate generating the match plan. In othercases, the prediction model 160 may be use to confirm whether aparticular match plan satisfies a set of conditions, such as, forexample, a particular threshold retention rate.

Example Retention Analysis System

FIG. 1C illustrates an embodiment of a retention analysis system 140 ofFIG. 1A. The retention analysis system 140 can apply or use one or moreof the prediction models 160 generated by the model generation system146. Although illustrated as a separate system, in some cases, theretention analysis system 140 may be included as part of the usermatching system 132. The retention analysis system 140 may use one ormore prediction models 160A, 160B, 160N (which may be referred tocollectively as “prediction models 160” or in the singular as“prediction model 160”) to process the input data 172 to obtain theoutput data 174.

The retention analysis system 140 may apply the prediction model(s) 160during initiation of game play or a match for a particular instance ofthe video game 112. In some embodiments, the prediction models 160 areapplied at the beginning of the game to generate a match plan or toconfirm that the match plan satisfies a particular condition, such as,for example, retention rate or likelihood that the users spend aparticular amount of in-game or real-world currency (for example, USdollars or Euros). In other embodiments, the prediction models 160 areapplied at different times during the game and/or at different stages inthe game. During initiation of an instance of the video game 112 orduring selection of a set of users to be teammates and/or opponents, theretention analysis system 140 receives input data 172 that can beapplied to one or more of the prediction models 160. The input data 172can include one or more pieces of data associated with a user who isplaying the video game 112 or has indicated a desire to play an instanceof the video game 112. This data may include user interaction data forthe video game 112, profile data for the user, and any other data thatmay be applied to the prediction model 160 to determine a retention orchurn rate for the user. Further, the input data 172 can include one ormore pieces of data associated with one or more additional users who maybe selected as opponents and/or teammates of the user. The dataassociated with the additional users may include the same type of dataas received for the user, a subset of the type of data received for theuser, and/or additional types of data than that received for the user.In some embodiments, the input data 172 can be filtered before it isprovided to the retention analysis system 140.

In some embodiments, a single prediction model 160 may exist for theretention analysis system 140. However, as illustrated, it is possiblefor the retention analysis system 140 to include multiple predictionmodels 160. The retention analysis system 140 can determine whichprediction model, such as any of models 160A-N, to use based on inputdata 172 and/or additional identifiers associated with the input data172. Additionally, the prediction model 160 selected may be selectedbased on the specific data provided as input data 172. The availabilityof particular types of data as part of the input data 172 can affect theselection of the prediction model 160. For example, the inclusion ofdemographic data (for example, age, gender, first language, or preferredlanguage) as part of the input data may result in the use of predictionmodel 160A. However, if demographic data is not available for aparticular user, then prediction model 160B may be used instead.

As mentioned above, one or more of the prediction models 160 may havebeen generated with or may be associated with a penalty 166. The penaltymay be used to impact the generation of the model or the selection of aprediction model for use by the retention analysis system 140.

The output data 174 can be a retention rate or churn rate associatedwith a prediction that a user, or a set of users, ceases to play thevideo game 112. For example, in some embodiments, the retention rate maybe between 0 and 100 indicating the predicted percentage of usersassociated with similar or the same data as included as input data 172who would cease to play the video game 112 within a threshold timeperiod. In some cases, the output data 174 may also identify a reasonfor the retention rate. For example, the retention analysis system 140may indicate that the 90% retention rate for a particular user is basedat least in part on the amount of money spent while playing the videogame 112. However, the retention analysis system 140 may indicate thatthe 90% retention rate for another user may be based at least in part onthe below freezing temperature in the geographic region where the useris located. As another example, the retention analysis system 140 mayindicate that the 20% retention rate for a user may be based at least inpart on the below 25% win ratio. In yet another example, the retentionanalysis system 140 may indicate that the 25% retention rate for a usermay be based at least in part on the skill level of the user's teammatesand/or opponents in a match plan not satisfying a skill level threshold.

The prediction models 160A, 160B, 160N may generally include a set ofone or more parameters 162A, 162B, 162N, respectively (which may bereferred to collectively as “parameters 162”). Each set of parameters162 (such as parameters 162A) may be combined using one or moremathematical functions to obtain a parameter function. Further, one ormore specific parameters from the parameters 162A, 1628, 162N may beweighted by the weights 164A, 1648, 164N (which may be referred tocollectively as “weights 164”). In some cases, the parameter functionmay be obtained by combining a set of parameters (such as the parameters162A) with a respective set of weights 164 (such as the weights 164A).Optionally, one or more of the prediction models 160A, 160B, 160N may beassociated with a penalty 166A, 1668, 166N, respectively (which may bereferred to collectively as “penalties 166”). It is recognized that theparameters 162 and/or the weights 164 used within the prediction model160 may be different from (in part or in whole) the parameters 162and/or the weights 164 used in the rule set 170 to generate or build theprediction model, and in some embodiments, the parameters and weights ofthe rule set for generating the model are different from the parametersand weights used within the model.

Example Prediction Model Generation Process

FIG. 2 presents a flowchart of an embodiment of a prediction modelgeneration process 200. The process 200 can be implemented by any systemthat can generate one or more parameter functions or prediction modelsthat include one or more parameters. In some cases, the process 200serves as a training process for developing one or more parameterfunctions or prediction models based on historical data or other knowndata. The process 200, in whole or in part, can be implemented by, forexample, an interactive computing system 130, a user matching system132, a skill evaluation system 136, a retention analysis system 140, amodel generation system 146, or a user computing system 110, amongothers. Although any number of systems, in whole or in part, canimplement the process 200, to simplify discussion, the process 200 willbe described with respect to particular systems. Further, it should beunderstood that the process 200 may be updated or performed repeatedlyover time. For example, the process 200 may be repeated once per month,with the addition or release of a new video game, or with the additionof a threshold number of new users available for analysis or playing avideo game 112. However, the process 200 may be performed more or lessfrequently.

The process 200 begins at block 202 where the model generation system146 receives historical data 152 comprising user interaction data for anumber of users of the video game 112. This historical data 152 mayserve as training data for the model generation system 146 and mayinclude user demographics or characteristics, such as age, geographiclocation, gender, or socioeconomic class. Alternatively, or in addition,the historical data 152 may include information relating to a play styleof one or more users; the amount of money spent playing the video game112; user success or failure information with respect to the video game112 (for example, a user win ratio); a play frequency of playing thevideo game 112; a frequency of using particular optional game elements(for example, available boosts, level skips, in-game hints, power ups,and the like); the amount of real money (for example, U.S. dollars orEuropean euros) spent purchasing in-game items for the video game 112;or the like. In addition, the historical data 152 may include datarelating to one or more other users who played the video game 112 with auser from the number of users. In some cases, the historical data 152may comprise user interaction data and other user or video game relateddata for multiple sets of users where each set includes a group of usersthat play a multiplayer instance of a video game together as opponents,teammates, or both. The user or video game data may include not only theabove-mentioned data, but also skill information for each user withrespect to the video game 112 and/or one or more actions that can beperformed in the video game 112 and/or one or more elements (such aslevels or obstacles) of the video game 112. In addition, the data mayinclude in-game character selection preferences, role preferences, andother information that can be used to distinguish play styles,preferences, or skills of different users.

At block 204, the model generation system 146 receives control data 156indicating a desired prediction criteria corresponding to the historicaldata 152. This control data 156 may indicate one or more features orcharacteristics for which the model generation system 146 is todetermine a model. Alternatively, or in addition, the control data 156may include a value for the features or characteristics that areassociated with the received historical data 152. In some embodiments,the control data 156 may include multiple characteristics or features tobe predicted by the model to be generated by the model generation system146. For example, the control data 156 may be a request to predict thelikelihood that a user continues to play the videogame for a particularperiod of time after playing an instance of the video game with a typeof paired user. As another example, the control data 150 may includelikelihood that a user, if paired with another user as a teammate oropponent, will complete a game within 2 hours or play continuously forat least 2 hours. The control data 156 may designate criteria to use todetermine churn rate, or retention rate, as the desired feature to bepredicted by the model that is to be generated by the model generationsystem 146. The criteria may identify churn rate of an individual and/orchurn rate of an individual as to another player type (for example,opponent or teammate). The churn rate or retention rate may correspondto a percentage of users associated with the historical data 152 thatceased playing the video game 112. Further, the control data 156 mayidentify criteria for determining a retention rate associated with thehistorical data. For example, the control data 156 may be used toidentify, for the users whose data was provided as the historical data152, both a retention rate and a reason for the retention rate (such asthe skill level of the opponents diverging by more than a thresholdskill delta, or a higher than threshold percentage of the teammatesand/or opponents quitting an instance of the video game 112 before thematch is completed), or a retention rate and an average monetary amountspent by the users whose data was provided as the historical data 152.

At block 206, the model generation system 146 generates one or moreprediction models 160 based on the historical data 152 and the controldata 156. The prediction models 160 may include one or more variables orparameters 162 that can be combined using a mathematical algorithm ormodel generation ruleset 170 to generate a prediction model 160 based onthe historical data 152 and, in some cases, the control data 156.Further, in certain embodiments, the block 206 may include applying oneor more feedback data 154. For example, if the prediction model 160 isgenerated as part of a supervised machine learning process, a systemuser (for example, an administrator) may provide one or more inputs tothe model generation system 146 as the prediction model 160 is beinggenerated and/or to refine the prediction model 160 generation process.For example, the user may be aware that a particular region orgeographic area had a power outage. In such a case, the system user maysupply feedback data 154 to reduce the weight of a portion of thehistorical data 152 that may correspond to users from the affectedgeographic region during the power outage. Further, in some cases, oneor more of the variables or parameters may be weighted using, forexample, weights 164. The value of the weight for a variable may bebased at least in part on the impact the variable has in generating theprediction model 160 that satisfies, or satisfies within a thresholddiscrepancy, the control data 156 and/or the historical data 152. Insome cases, the combination of the variables and weights may be used togenerate a prediction model 160.

Optionally, at block 208, the model generation system 146 applies apenalty to or associates a penalty 166 with at least some of the one ormore prediction models 160 generated at block 206 or with one or more ofthe variables used when generating the prediction models. The penaltyassociated with each of the one or more prediction models 160 maydiffer. Further, the penalty for each of the prediction models 160 maybe based at least in part on the model type of the prediction model 160and/or the mathematical algorithm used to combine the parameters 162 ofthe prediction model 160, and the number of parameters included in theparameter function. For example, when generating a prediction model 160,a penalty may be applied that disfavors a very large number of variablesor a greater amount of processing power to apply the model. As anotherexample, a prediction model 160 that uses more parameters or variablesthan another prediction model may be associated with a greater penalty166 than the prediction model that uses fewer variables. As a furtherexample, a prediction model that uses a model type or a mathematicalalgorithm that requires a greater amount of processing power tocalculate than another prediction model may be associated with a greaterpenalty than the prediction model that uses a model type or amathematical algorithm that requires a lower amount of processing powerto calculate.

The model generation system 146, at block 210, based at least in part onan accuracy of the prediction model 160 and any associated penaltyselects a prediction model 160. In some embodiments, the modelgeneration system 146 selects a prediction model 160 associated with alower penalty compared to another prediction model 160. However, in someembodiments, the model generation system 146 may select a predictionmodel associated with a higher penalty if, for example, the output ofthe prediction model 160 is a threshold degree more accurate than theprediction model associated with the lower penalty. In certainembodiments, the block 210 may be optional or omitted. For example, insome cases, the prediction models 160 may not be associated with apenalty and/or consideration of the penalty was done while generatingthe model. In some such cases, a prediction model may be selected from aplurality of prediction models based on the accuracy of the outputgenerated by the prediction model or may be selected at random. In othercases, the result of performing the block 206 is a single predictionmodel making the operations associated with the blocks 208 and 210unnecessary.

Example Matchmaking Objective

In embodiments described herein, matchmaking may be applied to a pool ofusers or players, P={p₁, . . . , p_(N)}, who are waiting to start orplay 1-vs-1 matches. One such example of a pool of users P isillustrated in FIG. 3 as the pool of users 302. For simplicity, the1-vs-1 use case is described. However, it should be understood thatembodiments herein can be expanded to include more users for amultiplayer match. A graph G can be constructed to model the set ofplayers waiting to play the multiplayer video game. One such example ofthe graph G is illustrated in FIG. 3 as the graph 304. Each player p_(i)may be represented by a vertex or a node of the graph, which has acurrent player state s_(i). The player state s_(i) may represent anydata specific to the user and the user's interaction with the video game112. For example, the player state s_(i) may represent the userinteraction data, the user's win-loss record, or user demographic data.Further, the player state data used may vary depending on the desiredobjective function, such as churn risk or likelihood to purchase in-gamecurrency. The edge between two players p_(i) and p_(i) may be associatedwith the expected sum objective or engagement metric (for example, sumchurn risk) if the users are paired. This metric relies on both users'states and may be denoted as a function ƒ(s_(i), s_(j)). Note that G istypically a complete graph in that all pairs of players can beconnected. However, a pre-computation and pre-filtering process may beperformed to reduce the graph. A list of user or player tuples,M={(p_(i), p_(j))}, may be used to denote a matchmaking result, or apair assignment, in which all players in P are paired and are onlypaired once. The graph 304 in FIG. 3, illustrative of the graph G,represents a graphical pairing of ten of the users from the pool 302,illustrative of the pool P. However, in some cases, the graph G may beformed to represent all players in the player pool P.

Embodiments described herein attempt to find an optimal pair assignmentM*, which maximizes the overall player engagement:M*=arg max_(M) Σ_((p) _(i) _(,p) _(j) )∈Mƒ(s _(i) , s _(j))  (1)

Churn risk can be used as a concrete disengagement metric. Churn riskcan indicate the likelihood of a player playing zero games within aparticular period of time or after a match played with the selectedmatch plan. Maximizing the sum engagement of two players can beconsidered equivalent to minimizing the sum churn risks of the twoplayers or users. The churn risk c_(i,j) of a player p_(i) after beingmatched in an instance of the video game with player p_(j) can bemodeled as a function of both users or players'states, c_(i,j)=c(s_(i),s_(j)). Note that typically c_(ij)!=c_(j,i) as two players in a pairedmatch may be impacted differently by being paired together. For example,one player may enjoy the matchup while the other player may not enjoythe matchup because each player may have different matchup preferences.Thus, using churn risk as the objective function and taking into accountthat each player's churn risk in a pairing may be affected differentlyby the pairing, the optimization objective function of equation 1 may beconverted to:M*=arg min_(M) Σ_((p) _(i) _(,p) _(j) )∈Mc(s _(i) , s _(j))+c(s _(j) , s_(i))  (2)

Assigning c_(i,j)+c_(j,i) as weights to the edge of G, the optimizationequation 2 can be converted to a minimum weight perfect matching (MWPM)problem. Solving the MWPM problem with respect to the graph G results infinding a pair assignment for the users P with the minimal sum weightsof edges.

Prediction of Churn Risk

The function c_(i,j)=c(s_(i), s_(j)) may be modeled as a churnprediction problem. The churn risk c_(i,j) of player p_(i) may depend onthe features from both the player himself and his or her opponent.However, using the player state information, or user interactioninformation, of both users as input to the function c_(i,j), which maybe modeled as a parameter function of prediction model obtained using amachine learning process, will double the feature dimensions of thefunction, which can make the resultant prediction unintelligible orharder to determine because, for example, generating the parameterfunction using a machine learning algorithm may require significantlymore training data compared to a function determined for a single user'splayer state information.

Thus, embodiments disclosed herein rely on a single user, p_(i)'s, stateinformation, s_(i), to predict c_(i,j). But the state information s₁ ofthe user's opponent may influence the state information s_(i) of theuser. For example, in the context of matchmaking, the predicted matchoutcome of an instance of a video game between the user p_(i) and theopponent p_(i) may be used to influence the determination of c_(i,j).Thus, once the game outcome is known, c_(i,j) may become conditionallyindependent on the opponent's state s_(j).

In one example use case, assume game outcomes are sampled from a finiteset O, such as Win, Lose and Draw. Using a standard skill model, a gameoutcome based on two users'skills can be predicted. In some cases, thisprediction can be regardless of other features of the users'states.Denoting player p_(i)'s skill representation (for example, via a vector)as μ_(i), the probability of the game outcome o_(i,j) between playerp_(i) and p_(j) (from the view of p_(i)) can be represented as:Pr(o _(i,j) |s _(i) , s _(j))=Pr(o _(i,j)|μ₁μ_(j))  (3)where o_(i,j)=W means that p_(i) wins and p_(i) loses, and whereo_(j,i)=L represents the same outcome from the view of p_(i).

The churn risk of paired users can be efficiently predicted based onequation 2 as follows:c(s _(i) , s _(j))+c(s _(j) , s _(i))  (4)=Σ_(o) _(i,j) _(∈O) Pr(o _(i,j) |s _(i) , s _(j))(c(s _(i) , s _(j) |o_(i,j))+c(s _(j) , s _(i) |o _(j,i)))  (5)=Σ_(o) _(i,j) _(∈O) Pr(o _(i,j)|μ_(i), μ_(j))(c(s _(i) |o _(i,j))+c(s_(j) |o _(j,i)))  (6)where the first equality is a marginalization on game outcome o_(i,j).In the second equality, equation 3 is plugged in. The constructc(s_(i)|o_(i,j)) may represent the churn risk of player pi aftermatchmaking, where the conditional independence of c_(i,j) on s_(j)given o_(i,j) is used.

The construct c(s_(i)|o_(i,j)) can be efficiently learned as a churnprediction problem. The input features may include an updated user stateafter determining the matchmaking video game outcome, s_(i)^(update)←s_(i)+o^(i,j). Decomposing s_(i)=[o_(i) ^(K), ŝ_(i)], where oris a vector of the latest K video game outcomes (for example, o_(i)^(K)=LWLDL when K=5), and ŝ_(i) represents the other state informationin s_(i). The state update can be represented as:s _(i) ^(update) ←s _(i) +o _(i,j)  (7)=[o _(i) ^(K) , ŝ _(i) ]+o _(i,j)  (8)=[o _(i) ^(k+1) , ŝ _(i) ^(update)]  (9)with ŝ_(i) ^(update) indicating that non-game-outcome state informationor features may also be updated after a new match is completed. Forexample, the total number of games played increments by 1. Embodimentsherein are amenable to adopting other churn prediction models based onthe updated user state information.

Once a graph of the users is generated and the weights are applied tothe edges based on the selected objective function, such as churn risk,an optical pair assignment can be determined using an edge selectionalgorithm. For example, solving a minimum weight perfect matching (MWPM)problem can be used to select the edges that result in user pairs beingselected that are associated with the lowest churn risk. The process 400described with respect to FIG. 4 below provides additional detailsregarding the selection of users for a match plan using the increasedengagement or churn risk reduction process.

Example Multiplayer Matching Process

FIG. 4 presents a flowchart of an embodiment of a multiplayer matchingprocess 400. The process 400 can be implemented by any system that cancreate a match plan of two or more users that may play the video game112 as opponents, teammates, or a combination of the two. The process400, in whole or in part, can be implemented by, for example, aninteractive computing system 130, a user matching system 132, a usergraph generator 120, a skill evaluation system 136, a retention analysissystem 140, a model generation system 146, or a user computing system110, among others. Although any number of systems, in whole or in part,can implement the process 400, to simplify discussion, the process 400will be described with respect to particular systems. Further, it shouldbe understood that the process 400 may be updated or performedrepeatedly over time. For example, the process 400 may be repeated foreach play session of a video game 112 or for each round of the videogame 112. However, the process 400 may be performed more or lessfrequently.

The process 400 begins at block 402 where the user matching system 132identifies a set of users waiting to play a multiplayer video game 112.The identified set of users or pool of players include users that maypotentially be paired or included in a match plan of two or more usersas teammates, opponents, or a combination of the two. The user matchingsystem 132 may use any type of system or process for identifying usersto potentially be matched together. For example, the user matchingsystem 132 may identify users based on their position or length of timein a queue of users waiting to play an instance of the video game 112.Alternatively, the user matching system 132 may identify the users basedon a skill level. In another example, the user matching system 132 mayselect users at random within a certain time window. This time windowmay be related to the length of time that a set of users have beenwaiting in a queue of users. Moreover, the time window may vary based onthe number of users in the queue of users. Further, in some cases, atleast some of the number of users may be selected based on an indicationthat the at least some of the number of users have indicated a desire toplay the video game 112 together.

At block 404, the user graph generator creates a fully connected graph,G, to model the users with each user from the set of users identified atthe block 402 representing a vertex or node within the graph and witheach vertex connected by an edge to each other vertex within the graph.FIG. 3 presents an example of the graph generation process. FIG. 3includes a pool of users 302. In the illustrated example, the pool ofusers 302 includes 14 users. Further, in the illustrated example of FIG.3, 10 users may be selected from the pool of users 302 as part of theoperations performed at the block 402 of the process 400.

As part of the operations performed at the block 404, a graph 304 may begenerated with each vertex of the graph representing one of the 10 usersselected at the block 402. As illustrated in FIG. 3, the graph 304 is afully connected graph with each vertex of the graph connected to eachother vertex of the graph. In other words, in the illustrated example,each vertex is connected to nine other vertexes in the graph 304.

Returning to FIG. 4, at block 406, the retention analysis system 140accesses user interaction data for each of the users identified at theblock 402. The user interaction data may be accessed from a user datarepository 142 may include information relating to each user'sinteraction with the video game 112. The user interaction data mayinclude current user interaction data, historical user interaction data,or a combination of the two. The current user interaction data mayinclude user interaction data from the current play session or userinteraction data that is newer than a particular threshold time period.For example, the recent user interaction data may include userinteraction data that is less than a week or a month old. Alternatively,or in addition, the recent user interaction data may include data fromplay sessions that are less than 3, 5, or 10 play sessions old.Conversely, the historical user interaction data may include userinteraction data that is older than a particular threshold time period.For example, the historical user interaction data may include userinteraction data that is at least a week or a month old. Alternatively,or in addition, the historical user interaction data may include datafrom play sessions that are more than 5 or 10 play sessions old.

The user interaction data may include any data relating to the user'sinteraction with the video game 112 including, for example, an identityof an in game character selected by the user; a role that the user playsa threshold percentage of times (such as a healer or a defender); anamount of time the user has spent playing the video game; an amount ofmoney the user has spent with respect to the video game 112; a skilllevel associated with the user; and the like. Further, block 406 mayinclude accessing data relating to opponents or teammates that the userhas previously played with. In some cases, the user interaction data fora user may include opponent or teammate-dependent data. For example, theuser interaction data for a user may indicate that the user typically(for example, more often than a threshold percentage) plays the videogame 112 as a healer when the user's teammates are of a higher skilllevel than the user. However, user interaction data for the user mayindicate that the user typically plays the video game 112 is a meleeattack character when the user's teammates are of a lower skill levelthan the user.

In certain embodiments, particular values (such as default values) maybe assigned to users who are not associated with user interaction data,or that are associated with user interaction data derived from less thana threshold number of matches or amount of playing with of the videogame 112. Advantageously, in some such embodiments, the process 400 maybe used with users that are new to playing the video game 112 or areassociated with less than a threshold amount of matches or play time forthe video game 112 by using the particular values for the user.

Optionally, at block 408, the user graph generator 120 performs apre-computation process for each user based at least in part on the userinteraction data of the user to predict a user desired match outcome foreach user. Performing the pre-computation process may include using aparameter function or prediction model 160 to predict a user's desiredmatch outcome based on the user interaction data of the user. Thisprediction model may be a different model than used in block 414.Determining the users desired match outcome may include determiningwhether the user desires to win or lose his or her next match of thevideogame 112, or whether the user is indifferent to the outcome of thenext match. For example, a user who has won several matches in a row maybegin to feel the videogame 112 is too simple and may desire to lose thenext match to maintain the feeling of a challenge. Another user maydislike losing and, despite having won several matches in a row, maydesire to continue winning matches. A third user may have lost severalmatches in a row and may desire to win a match. If the third user doesnot win a match the video game 112 may appear too challenging to thethird user. A fourth user may enjoy playing the videogame 112 regardlessof the outcome.

At block 410, the user graph generator 120 performs a pre-filteringprocess on the fully connected graph created at the block 404 based atleast in part on the user desired match outcome for each user determinedat the block 408 to obtain a reduced graph. Performing the pre-filteringprocess may include removing edges between particular users within thepreviously fully connected graph. For example, if it is determined thatone user has a desire to win the next match and one user has a desire tolose the next match, an edge connecting a pair of nodes representativeof the two users as being potential teammates may be removed from theconnected graph. Advantageously, in certain embodiments, by performingthe pre-filtering process to reduce the fully connected graph to areduced graph, the amount of processing power required to performoperations related to subsequent blocks in the process 400 is reduced.For example, as will be described in more detail below, the block 418includes solving a minimum weight matching problem to identify one ormore match plans for playing the videogame 112. By performing thepre-filtering process to reduce the fully connected graph, operationsassociated with the block 418 may be performed more quickly and withless computational resources on the reduced connected graph obtained bythe pre-filtering process compared to performing the operations on thefully connected graph. In certain embodiments, the block 410 is optionalor omitted, such as when the block 408 is omitted.

At block 412, for each edge in the reduced graph, or the fully connectedgraph if the blocks 408 and 410 are omitted, the retention analysissystem 140 accesses user interaction data for the pair of userscorresponding to the vertex pair connected by the edge. Each edge may berepresentative of one versus one match plan. Thus, the edges in thegraph the graph may be thought of as a plurality of potential one versusone match plans. While the process 400 is primarily described withrespect to identifying pairs of users to play, for example, one versusone matches of the videogame 112, the process 400 is not limited assuch. Embodiments of the process 400 may be used to create match planthat includes more than two users. For example, the process 400 may beused to select three or four users to include in a match plan. Thus, insome embodiments, the retention analysis system 140 accesses userinteraction data for a set of users corresponding to the verticesconnected by a set of edges included in a match plan. For example,suppose that a particular videogame 112 supports three users playingagainst three non-player characters (NPCs). In such cases, userinteraction data may be selected for triangles of users within the fullyconnected or reduced graph.

Using a prediction model at block 414, the retention analysis system 140calculates a churn risk for each user from each pair of users in theconnected graph based at least in part on the user interaction data forthe pair of users. The churn risk may include a probability that theuser continues to play the videogame 112 for a particular period of timeafter playing an instance of the videogame 112 with the paired user. Insome cases, determining the churn risk for each user includes applyingthe predicted outcome of a matchup between the pair of users and theuser interaction data for a user to the prediction model. Alternatively,or in addition, determining the churn risk for each user includesapplying the interaction data for the user and the interaction data forthe paired user, or groups of users in the case of multiplayer matchupsthat include more than two users, to the prediction model.

As previously described, the user interaction data may include any typeof data that relates to the user's interaction with the videogame 112including the actions taken by the user while playing the videogame 112and the users level of success are playing the videogame 112. In somecases, the user interaction data may include additional, or alternativedata, relating to the user's preferences when playing the videogame 112.Moreover, the user interaction data available for one user may differfrom the user interaction data available for another user. For example,a preferred character class may be known for one user and included inthe user interaction data, but may not be known for another user and maytherefore be omitted from the user interaction data for the other user.Furthermore, in some cases, the particular match plan detailing whichplayers are teammates and which players are opponents may be provided tothe prediction model 160 to determine the churn risk for each of thenumber of users. In some such cases, the prediction model 160 mayoutput, in addition to or instead of the churn risk, a match plan or analternative match plan that maximizes the retention rate for the set ofusers. In some cases, the user interaction data may include providingadditional data indicating that two or more users that are to be groupedtogether as teammates or as opponents. For example, two players mayindicate that they desire to play a match together. Thus, in some cases,the parameter function may adjust the outputted churn risk for the pairor group of users based on an indication that two or more of users hasindicated a desire to play the match together.

At block 416, the user graph generator 120 assigns a weight to each edgeof the graph based on a sum of the churn risks for the pair of userscorresponding to the vertex pair connected by the edge, for example, thesum of the churn risk for player A being paired with player B and thechurn risk for player B being paired with player A. In some cases, apair of users may indicate a desire to play match together. In suchcases, the weight assigned to an edge associated with the pair of usersthat have indicated a desire to play a match together may be adjusted toensure that the edge is selected as part of the process associated withthe block 418 described below. In cases where the number of users thathave indicated a desire to play match together is less than the totalnumber of users to play a match, nodes associated with the users aremaintained within the graph for the purpose of selecting the additionalusers to play the match. For example, in a four player multiplayervideogame 112, if two users indicate a desire to play together nodesassociated with the two users may be maintained within the graph and anedge between the two users may be weighted such that selection of thetwo users to play together as guaranteed. However, if the number ofusers who indicated a desire to play the multiplayer video game 112together is equal to the number of users that may play a multiplayervideogame 112 together, nodes and edges associated with the users thathave indicated a desire to play together may be removed from the graph.In other cases, users associated with particular nodes within the graphmay be replaced with other users.

At block 418, the user matching system 132 solves a minimum weightmatching (MWM) or a minimum weight perfect matching (MWPM) problem usingthe assigned weights to determine a set of edges. In cases where an MWMproblem is solved, the selected edges may omit one or more of the nodesincluded in the graph. In such cases, users associated with omittednodes may be included in another graph with alternative users in asubsequent performance of the process 400. In other words, in somecases, not all users identified in the block 402 may be assigned to aninstance of the videogame 112. In these cases, the users may be placedback in a pool of users awaiting assignment to an instance of thevideogame 112. In cases where an MWPM and problem is solved, each nodein the graph will be associated with a selected edge.

As previously stated, embodiments disclosed herein may be applied toadditional or alternative objectives and are not limited to theselection of users based on churn risk. For example, at block 414, aprediction model may be used to determine retention rate, currencyspending rate, probability of positive or negative user behavior asdetermined, for example, by the video game developer or a video gamecommunity, or the probability a user completes a match alone or withrespect to one more teammate types or opponent types. In some cases, itmay be desirable to select edges associated with the highest weightsrather than the lowest weights. In such cases, the block 418 may includesolving a maximum weight matching or a maximum weight perfect matchingproblem.

In some embodiments, the block 418 also includes identifying usersassociated with vertices of the set of edges to play an instance of thevideogame 112. In some cases, the pair of users associated with eachedge are identified for playing a separate instance of the videogame112. In other cases, three or more users connected by selected edgeswithin the graph may be selected for playing an instance of thevideogame 112. For example, in a three player multiplayer game, theblock 418 may include solving the minimum weight matching problem forgroups of three edges instead of individual edges. Users associated witheach group, or triangle, of three edges may be selected to play aninstance of the videogame 112. In other words, the process 400 may bemodified from determining users to play an instance of the videogamebased on a state of a user and the state of an opponent to a groupingproblem that examines the predicted churn risk for three or more usersselected to play the instance of the videogame 112 together.

The graph 404 of FIG. 4 illustrates the completion of the solving of theminimum weight matching problem as illustrated by the bolded or thickerlines that exist between pairs of users in the graph 404. In someembodiments, the selected edges may be filtered to remove edges that donot satisfy a particular threshold. For example, continuing the exampleillustrated in FIG. 4, solving the minimum weight matching problem mayresult in selecting the five edges with the lowest associated weights.However, some of the five edges may be associated with the weights thatexceed a particular threshold. In some such cases the edges associatedweights that exceed the particular threshold may be removed and theassociated users may be included in a new graph with additional usersfrom the pool of users 402.

At block 420, the user matching system 132 and initiates a playableinstance of the video game 112 using a pair of users associated with aselected edge from the set of edges determined at the block 418. In somecases, the block 420 may include initiating a separate playable instanceof the videogame 112 for each pair of users associated with eachselected edge from the set of selected edges. Moreover, as describedwith respect to the block 418, more than two users may be selected toplay an instance of the videogame 112. The grouping of three or moreusers may be selected based on the users' association with a selectedgroup of edges from the set of edges.

In some embodiments, the block 420 may include determining whether thechurn risk for each pair of users associated with each selected edgesatisfies a threshold. For each pair of users associated with a churnrisk that does satisfy the threshold, a playable instance of thevideogame 112 may be initiated. Conversely, for each pair of usersassociated with a churn risk that does not satisfy the threshold, thepair of users may be placed back in a queue of users waiting to play aninstance of the videogame 112.

In certain embodiments, at least some of the operations associated withat least some of the blocks of the process 400 may be performed inadvance of a trigger to initiate an instance of the videogame 112. Forexample, operations associated with the blocks 402 through 416 may beperformed during a first time period. During a second time periodsubsequent to the first time period, upon receiving a trigger toinitiate one or more playable instances of the multiplayer video game112, operations associated with the blocks 418 and 420 may be performed.Advantageously, by performing at least some of the operations inadvance, the process of matching users for playing an instance of thevideogame 112 may be accelerated. Further, portions of the process 400may be repeated at a lower computational cost. For example, suppose theoperations associated with the blocks 402 through 412 are performedduring a first time period. Further, suppose that one or more usersidentified at the block 402 cease playing overseas waiting to play aninstance of the multiplayer video game. In such cases, the operationsassociated with the blocks 402 through 412 may be repeated during thefirst time period before performing the subsequent blocks during asecond time period. By separating the performance of certain blocks intodifferent time periods, fewer operations may need to be repeated upondetecting a change in the available users, thereby improving theperformance of the user selection process.

Overview of Computing System

FIG. 5 illustrates an embodiment of a user computing system 110, whichmay also be referred to as a gaming system. Although FIG. 5 is specificto the user computing system 110, it should be understood that the usercomputing systems 114 and 116 may have the same or a similarconfiguration. Alternatively, one or more of the user computing systems114 and 116 may have different configurations than each other and/or theuser computing system 110. As illustrated, the user computing system 110may be a single computing device that can include a number of elements.However, in some cases, the user computing system 110 may includemultiple devices. For example, the user computing system 110 may includeone device that includes a central processing unit and a graphicsprocessing unit, another device that includes a display, and anotherdevice that includes an input mechanism, such as a keyboard or mouse.

The user computing system 110 can be an embodiment of a computing systemthat can execute a game system. In the non-limiting example of FIG. 5,the user computing system 110 is a touch-capable computing devicecapable of receiving input from a user via a touchscreen display 502.However, the user computing system 110 is not limited as such and mayinclude non-touch capable embodiments, which do not include atouchscreen display 502.

The user computing system 110 includes a touchscreen display 502 and atouchscreen interface 504, and is configured to execute a gameapplication. This game application may be the video game 112 or anapplication that executes in conjunction with or in support of the videogame 112, such as a video game execution environment. Although describedas a game application 112, in some embodiments the application 112 maybe another type of application that may be capable of interacting withmultiple users across multiple user computing systems, such aseducational software or language software. While user computing system110 includes the touchscreen display 502, it is recognized that avariety of input devices may be used in addition to or in place of thetouchscreen display 502.

The user computing system 110 can include one or more processors, suchas central processing units (CPUs), graphics processing units (GPUs),and accelerated processing units (APUs). Further, the user computingsystem 110 may include one or more data storage elements. In addition,the user computing system 110 may include one or more volatile memoryelements, such as random-access memory (RAM). In some embodiments, theuser computing system 110 can be a specialized computing device createdfor the purpose of executing game applications 112. For example, theuser computing system 110 may be a video game console. The gameapplications 112 executed by the user computing system 110 may becreated using a particular application programming interface (API) orcompiled into a particular instruction set that may be specific to theuser computing system 110. In some embodiments, the user computingsystem 110 may be a general purpose computing device capable ofexecuting game applications 112 and non-game applications. For example,the user computing system 110 may be a laptop with an integratedtouchscreen display or desktop computer with an external touchscreendisplay. Components of an example embodiment of a user computing system110 are described in more detail with respect to FIG. 6.

The touchscreen display 502 can be a capacitive touchscreen, a resistivetouchscreen, a surface acoustic wave touchscreen, or other type oftouchscreen technology that is configured to receive tactile inputs,also referred to as touch inputs, from a user. For example, the touchinputs can be received via a finger touching the screen, multiplefingers touching the screen, a stylus, or other stimuli that can be usedto register a touch input on the touchscreen display 502. Thetouchscreen interface 504 can be configured to translate the touch inputinto data and output the data such that it can be interpreted bycomponents of the user computing system 110, such as an operating systemand the game application 112. The touchscreen interface 504 cantranslate characteristics of the tactile touch input touch into touchinput data. Some example characteristics of a touch input can include,shape, size, pressure, location, direction, momentum, duration, and/orother characteristics. The touchscreen interface 504 can be configuredto determine the type of touch input, such as, for example a tap (forexample, touch and release at a single location) or a swipe (forexample, movement through a plurality of locations on touchscreen in asingle touch input). The touchscreen interface 504 can be configured todetect and output touch input data associated with multiple touch inputsoccurring simultaneously or substantially in parallel. In some cases,the simultaneous touch inputs may include instances where a usermaintains a first touch on the touchscreen display 502 whilesubsequently performing a second touch on the touchscreen display 502.The touchscreen interface 504 can be configured to detect movement ofthe touch inputs. The touch input data can be transmitted to componentsof the user computing system 110 for processing. For example, the touchinput data can be transmitted directly to the game application 112 forprocessing.

In some embodiments, the touch input data can undergo processing and/orfiltering by the touchscreen interface 504, an operating system, orother components prior to being output to the game application 112. Asone example, raw touch input data can be captured from a touch input.The raw data can be filtered to remove background noise, pressure valuesassociated with the input can be measured, and location coordinatesassociated with the touch input can be calculated. The type of touchinput data provided to the game application 112 can be dependent uponthe specific implementation of the touchscreen interface 504 and theparticular API associated with the touchscreen interface 504. In someembodiments, the touch input data can include location coordinates ofthe touch input. The touch signal data can be output at a definedfrequency. Processing the touch inputs can be computed many times persecond and the touch input data can be output to the game applicationfor further processing.

A game application 112 can be configured to be executed on the usercomputing system 110. The game application 112 may also be referred toas a video game, a game, game code and/or a game program. A gameapplication should be understood to include software code that a usercomputing system 110 can use to provide a game for a user to play. Agame application 112 might comprise software code that informs a usercomputing system 110 of processor instructions to execute, but mightalso include data used in the playing of the game, such as data relatingto constants, images and other data structures. For example, in theillustrated embodiment, the game application includes a game engine 512,game data 514, and game state information 516. As previously stated, theembodiments described herein may be used for applications other thanvideo games, such as educational software or videoconferencing. Thus, insome such cases, the game application 112 may be substituted with othertypes of applications that may involve multiple users communicating overa network and selecting a server, or one of the plurality of usercomputing systems, to act as a host.

The touchscreen interface 504 or another component of the user computingsystem 110, such as the operating system, can provide user input, suchas touch inputs, to the game application 112. In some embodiments, theuser computing system 110 may include alternative or additional userinput devices, such as a mouse, a keyboard, a camera, a game controller,and the like. Further, the user computing system 110 may include avirtual reality display and/or an augmented reality display. A user caninteract with the game application 112 via the touchscreen interface 504and/or one or more of the alternative or additional user input devices.The game engine 512 can be configured to execute aspects of theoperation of the game application 112 within the user computing system110. Execution of aspects of gameplay within a game application can bebased, at least in part, on the user input received, the game data 514,and game state information 516. The game data 514 can include gamerules, prerecorded motion capture poses/paths, environmental settings,constraints, animation reference curves, skeleton models, and/or othergame application information. Further, the game data 514 may includeinformation that is used to set or adjust the difficulty of the gameapplication 112.

The game engine 512 can execute gameplay within the game according tothe game rules. Some examples of game rules can include rules forscoring, possible inputs, actions/events, movement in response toinputs, and the like. Other components can control what inputs areaccepted and how the game progresses, and other aspects of gameplay.During execution of the game application 112, the game application 112can store game state information 516, which can include characterstates, environment states, scene object storage, and/or otherinformation associated with a state of execution of the game application112. For example, the game state information 516 can identify the stateof the game application at a specific point in time, such as a characterposition, character action, game level attributes, and other informationcontributing to a state of the game application.

The game engine 512 can receive the user inputs and determine in-gameevents, such as actions, collisions, runs, throws, attacks and otherevents appropriate for the game application 112. During operation, thegame engine 512 can read in game data 514 and game state information 516in order to determine the appropriate in-game events. In one example,after the game engine 512 determines the character events, the characterevents can be conveyed to a movement engine that can determine theappropriate motions the characters should make in response to the eventsand passes those motions on to an animation engine. The animation enginecan determine new poses for the characters and provide the new poses toa skinning and rendering engine. The skinning and rendering engine, inturn, can provide character images to an object combiner in order tocombine animate, inanimate, and background objects into a full scene.The full scene can be conveyed to a renderer, which can generate a newframe for display to the user. The process can be repeated for renderingeach frame during execution of the game application. Though the processhas been described in the context of a character, the process can beapplied to any process for processing events and rendering the outputfor display to a user.

In some cases, at least some of the videogame engine 512 may reside on aserver, such as one of the videogame servers 152. Further, in somecases, the complete videogame engine 512 may reside on the server. Thus,in some cases, the videogame engine 512 may be omitted from the portionof the videogame application 112 hosted on the user computing system110. Similarly, in some embodiments, videogame state information 516 andvideogame data 514 may be hosted on a server in addition to or insteadof on the user computing system 110. Further, in some cases, actions ofthe user performed within the video game application 112 may betransmitted to a server that is hosting a portion of the videogame 112.The server may compute or determine the result of the user's interactionwith respect to the videogame application 112, such as collisions,attacks, or movements. The server may then send a result of the user'sactions to the videogame application 112 on the user computing system110. The videogame application 112 may then perform an action inresponse to the result, such as displaying the result to the user.

Example Hardware Configuration of Computing System

FIG. 6 illustrates an embodiment of a hardware configuration for theuser computing system 110 of FIG. 5. It should be understood that eachof the user computing systems 114 and 116 may be configured similarly orthe same as the user computing system 110. Alternatively, one or more ofthe user computing systems 114 and 116 may have different configurationsthan each other and/or the user computing system 110. Other variationsof the user computing system 110 may be substituted for the examplesexplicitly presented herein, such as removing or adding components tothe user computing system 110. The user computing system 110 may includea dedicated game device, a smart phone, a tablet, a personal computer, adesktop, a laptop, a smart television, a car console display, and thelike. Further, (although not explicitly illustrated in FIG. 6) asdescribed with respect to FIG. 5, the user computing system 110 mayoptionally include a touchscreen display 502 and a touchscreen interface504.

As shown, the user computing system 110 includes a processing unit 20that interacts with other components of the user computing system 110and also components external to the user computing system 110. A gamemedia reader 22 may be included that can communicate with game media 12.Game media reader 22 may be an optical disc reader capable of readingoptical discs, such as CD-ROM or DVDs, or any other type of reader thatcan receive and read data from game media 12. In some embodiments, thegame media reader 22 may be optional or omitted. For example, gamecontent or applications may be accessed over a network via the networkI/O 38 rendering the game media reader 22 and/or the game media 12optional.

The user computing system 110 may include a separate graphics processor24. In some cases, the graphics processor 24 may be built into theprocessing unit 20, such as with an APU. In some such cases, thegraphics processor 24 may share Random Access Memory (RAM) with theprocessing unit 20. Alternatively, or in addition, the user computingsystem 110 may include a discrete graphics processor 24 that is separatefrom the processing unit 20. In some such cases, the graphics processor24 may have separate RAM from the processing unit 20. Further, in somecases, the graphics processor 24 may work in conjunction with one ormore additional graphics processors and/or with an embedded ornon-discrete graphics processing unit, which may be embedded into amotherboard and which is sometimes referred to as an on-board graphicschip or device.

The user computing system 110 also includes various components forenabling input/output, such as an I/O 32, a user I/O 34, a display I/O36, and a network I/O 38. As previously described, the input/outputcomponents may, in some cases, including touch-enabled devices. The I/O32 interacts with storage element 40 and, through a device 42, removablestorage media 44 in order to provide storage for the user computingsystem 110. Processing unit 20 can communicate through I/O 32 to storedata, such as game state data and any shared data files. In addition tostorage 40 and removable storage media 44, the user computing system 110is also shown including ROM (Read-Only Memory) 46 and RAM 48. RAM 48 maybe used for data that is accessed frequently, such as when a game isbeing played, or for all data that is accessed by the processing unit 20and/or the graphics processor 24.

User I/O 34 is used to send and receive commands between processing unit20 and user devices, such as game controllers. In some embodiments, theuser I/O 34 can include touchscreen inputs. As previously described, thetouchscreen can be a capacitive touchscreen, a resistive touchscreen, orother type of touchscreen technology that is configured to receive userinput through tactile inputs from the user. Display I/O 36 providesinput/output functions that are used to display images from the gamebeing played. Network I/O 38 is used for input/output functions for anetwork. Network I/O 38 may be used during execution of a game, such aswhen a game is being played online or being accessed online.

Display output signals may be produced by the display I/O 36 and caninclude signals for displaying visual content produced by the usercomputing system 110 on a display device, such as graphics, userinterfaces, video, and/or other visual content. The user computingsystem 110 may comprise one or more integrated displays configured toreceive display output signals produced by the display I/O 36, which maybe output for display to a user. According to some embodiments, displayoutput signals produced by the display I/O 36 may also be output to oneor more display devices external to the user computing system 110.

The user computing system 110 can also include other features that maybe used with a game, such as a clock 50, flash memory 52, and othercomponents. An audio/video player 56 might also be used to play a videosequence, such as a movie. It should be understood that other componentsmay be provided in the user computing system 110 and that a personskilled in the art will appreciate other variations of the usercomputing system 110.

Program code can be stored in ROM 46, RAM 48, or storage 40 (which mightcomprise hard disk, other magnetic storage, optical storage, solid statedrives, and/or other non-volatile storage, or a combination or variationof these). At least part of the program code can be stored in ROM thatis programmable (ROM, PROM, EPROM, EEPROM, and so forth), in storage 40,and/or on removable media such as game media 12 (which can be a CD-ROM,cartridge, memory chip or the like, or obtained over a network or otherelectronic channel as needed). In general, program code can be foundembodied in a tangible non-transitory signal-bearing medium.

Random access memory (RAM) 48 (and possibly other storage) is usable tostore variables and other game and processor data as needed. RAM is usedand holds data that is generated during the play of the game andportions thereof might also be reserved for frame buffers, game stateand/or other data needed or usable for interpreting user input andgenerating game displays. Generally, RAM 48 is volatile storage and datastored within RAM 48 may be lost when the user computing system 110 isturned off or loses power.

As user computing system 110 reads game media 12 and provides a game,information may be read from game media 12 and stored in a memorydevice, such as RAM 48. Additionally, data from storage 40, ROM 46,servers accessed via a network (not shown), or removable storage media46 may be read and loaded into RAM 48. Although data is described asbeing found in RAM 48, it will be understood that data does not have tobe stored in RAM 48 and may be stored in other memory accessible toprocessing unit 20 or distributed among several media, such as gamemedia 12 and storage 40.

Additional Embodiments

It is to be understood that not necessarily all objects or advantagesmay be achieved in accordance with any particular embodiment describedherein. Thus, for example, those skilled in the art will recognize thatcertain embodiments may be configured to operate in a manner thatachieves, increases, or optimizes one advantage or group of advantagesas taught herein without necessarily achieving other objects oradvantages as may be taught or suggested herein.

All of the processes described herein may be embodied in, and fullyautomated via, software code modules executed by a computing system thatincludes one or more computers or processors. The code modules may bestored in any type of non-transitory computer-readable medium or othercomputer storage device. Some or all the methods may be embodied inspecialized computer hardware.

Many other variations than those described herein will be apparent fromthis disclosure. For example, depending on the embodiment, certain acts,events, or functions of any of the algorithms described herein can beperformed in a different sequence, can be added, merged, or left outaltogether (for example, not all described acts or events are necessaryfor the practice of the algorithms). Moreover, in certain embodiments,acts or events can be performed concurrently, for example, throughmulti-threaded processing, interrupt processing, or multiple processorsor processor cores or on other parallel architectures, rather thansequentially. In addition, different tasks or processes can be performedby different machines and/or computing systems that can functiontogether.

The various illustrative logical blocks and modules described inconnection with the embodiments disclosed herein can be implemented orperformed by a machine, such as a processing unit or processor, adigital signal processor (DSP), an application specific integratedcircuit (ASIC), a field programmable gate array (FPGA) or otherprogrammable logic device, discrete gate or transistor logic, discretehardware components, or any combination thereof designed to perform thefunctions described herein. A processor can be a microprocessor, but inthe alternative, the processor can be a controller, microcontroller, orstate machine, combinations of the same, or the like. A processor caninclude electrical circuitry configured to process computer-executableinstructions. In another embodiment, a processor includes an FPGA orother programmable device that performs logic operations withoutprocessing computer-executable instructions. A processor can also beimplemented as a combination of computing devices, for example, acombination of a DSP and a microprocessor, a plurality ofmicroprocessors, one or more microprocessors in conjunction with a DSPcore, or any other such configuration. Although described hereinprimarily with respect to digital technology, a processor may alsoinclude primarily analog components. A computing environment can includeany type of computer system, including, but not limited to, a computersystem based on a microprocessor, a mainframe computer, a digital signalprocessor, a portable computing device, a device controller, or acomputational engine within an appliance, to name a few.

Conditional language such as, among others, “can,” “could,” “might” or“may,” unless specifically stated otherwise, are otherwise understoodwithin the context as used in general to convey that certain embodimentsinclude, while other embodiments do not include, certain features,elements and/or steps. Thus, such conditional language is not generallyintended to imply that features, elements and/or steps are in any wayrequired for one or more embodiments or that one or more embodimentsnecessarily include logic for deciding, with or without user input orprompting, whether these features, elements and/or steps are included orare to be performed in any particular embodiment.

Disjunctive language such as the phrase “at least one of X, Y, or Z,”unless specifically stated otherwise, is otherwise understood with thecontext as used in general to present that an item, term, etc., may beeither X, Y, or Z, or any combination thereof (for example, X, Y, and/orZ). Thus, such disjunctive language is not generally intended to, andshould not, imply that certain embodiments require at least one of X, atleast one of Y, or at least one of Z to each be present.

Any process descriptions, elements or blocks in the flow diagramsdescribed herein and/or depicted in the attached figures should beunderstood as potentially representing modules, segments, or portions ofcode which include one or more executable instructions for implementingspecific logical functions or elements in the process. Alternateimplementations are included within the scope of the embodimentsdescribed herein in which elements or functions may be deleted, executedout of order from that shown, or discussed, including substantiallyconcurrently or in reverse order, depending on the functionalityinvolved as would be understood by those skilled in the art.

Unless otherwise explicitly stated, articles such as “a” or “an” shouldgenerally be interpreted to include one or more described items.Accordingly, phrases such as “a device configured to” are intended toinclude one or more recited devices. Such one or more recited devicescan also be collectively configured to carry out the stated recitations.For example, “a processor configured to carry out recitations A, B andC” can include a first processor configured to carry out recitation Aworking in conjunction with a second processor configured to carry outrecitations B and C.

It should be emphasized that many variations and modifications may bemade to the above-described embodiments, the elements of which are to beunderstood as being among other acceptable examples. All suchmodifications and variations are intended to be included herein withinthe scope of this disclosure.

What is claimed is:
 1. A computer-implemented method comprising: asimplemented by an interactive computing system configured with specificcomputer-executable instructions, selecting a plurality of users from apool of users, the pool of users available for selection to play aninstance of a video game, wherein at least a first portion of theinstance of the video game executes on a user computing device of atleast one user from the plurality of users and a second portion of theinstance of the video game executes on the interactive computing system;creating a connected graph comprising a plurality of vertexes and aplurality of edges, wherein each vertex is connected by at least oneedge from the plurality of edges and wherein each vertex represents adifferent user from the plurality of users such that each user from theplurality of users is paired within the connected graph with at leastone other user from the plurality of users; accessing user interactiondata for each user of the plurality of users, the user interaction datacorresponding to the user's interaction with the video game, wherein theuser interaction data for the user is selected from data that is morerecent than a particular time threshold; for each edge in the connectedgraph, assigning a weight to the edge based at least in part on a firstchurn risk of a first user corresponding to a first node of the edge anda second churn risk of a second user corresponding to a second node ofthe edge, wherein the first churn risk is based at least in part on theuser interaction data for the first user and the second churn risk isbased at least in part on the user interaction data for the second user;selecting a set of edges from the connected graph to obtain a set ofselected edges based at least in part on the weights assigned to eachedge within the connected graph, wherein each vertex in the connectedgraph is connected to at least one edge within the set of selectededges; and initiating a playable instance of the video game using atleast a pair of users corresponding to vertexes of one or more edgesincluded in the set of selected edges.
 2. The computer-implementedmethod of claim 1, wherein the connected graph is a fully connectedgraph.
 3. The computer-implemented method of claim 1, further comprisingdetermining the first churn risk by applying at least the userinteraction data of the first user to a parameter function generatedbased at least in part on a machine learning algorithm.
 4. Thecomputer-implemented method of claim 1, further comprising predicting amatch outcome for a particular instance of the video game played by thefirst user and the second user based at least in part on the userinteraction data of the first user and the user interaction data of thesecond user.
 5. The computer-implemented method of claim 4, furthercomprising determining the first churn risk by applying at least theuser interaction data of the first user and the predicted match outcometo a parameter function generated based at least in part on a machinelearning algorithm.
 6. The computer-implemented method of claim 1,wherein the weight comprises a summation or average of the first churnrisk and the second churn risk.
 7. The computer-implemented method ofclaim 1, wherein selecting the set of edges comprises solving a minimumweight matching problem for the connected graph based at least in parton the weights assigned to each edge of the connected graph.
 8. Thecomputer-implemented method of claim 1, further comprising, for eachuser of the plurality of users, performing a precomputation processbased at least in part on the user interaction data for the user todetermine a desired video game match outcome for the user.
 9. Thecomputer-implemented method of claim 8, further comprising: determininga fully connected graph with each vertex representing a different userfrom the plurality of users; and performing a pre-filtering process onthe fully connected graph based at least in part on the desired videogame match outcome for each user of the plurality of users to obtain theconnected graph.
 10. The computer-implemented method of claim 1,wherein: at least said creating the connected graph occurs during afirst time period; and at least said performing an edge selectionprocess occurs during a second time period later than the first timeperiod.
 11. The computer-implemented method of claim 10, wherein, inresponse to a change of availability of at least one user in theplurality of users, an updated plurality of users is selected from thepool of users and at least said creating the connected graph is repeatedduring the first time period using the updated plurality of users. 12.The computer-implemented method of claim 10, wherein the second timeperiod begins in response to a trigger to initiate the playable instanceof the video game.
 13. A system comprising: an electronic data storeconfigured to store user interaction data for users of a video game; ahardware processor in communication with the electronic data store, thehardware processor configured to execute specific computer-executableinstructions to at least: select a plurality of users available to playan instance of a video game, wherein at least a first portion of theinstance of the video game executes on a user computing device of atleast one user from the plurality of users and a second portion of theinstance of the video game executes on the interactive computing system;create a connected graph with each vertex representing a different userfrom the plurality of users such that each user from the plurality ofusers is paired within the connected graph with at least one other userfrom the plurality of users; access user interaction data from theelectronic data store for each user of the plurality of users, the userinteraction data corresponding to the user's interaction with the videogame, wherein the user interaction data for the user is selected fromdata that is more recent than a particular time threshold; for each edgein the connected graph, assign a weight to the edge based at least inpart on a first churn risk of a first user corresponding to a first nodeof the edge and a second churn risk of a second user corresponding to asecond node of the edge, wherein the first churn risk is based at leastin part on the user interaction data for the first user and the secondchurn risk is based at least in part on the user interaction data forthe second user; select a set of edges from the connected graph based atleast in part on the weights assigned to each edge within the connectedgraph, wherein each vertex in the connected graph is connected to atleast one edge within the set of selected edges; and initiate a playableinstance of the video game using at least two users corresponding tovertexes of one or more edges included in the set of selected edges. 14.The system of claim 13, wherein the hardware processor is furtherconfigured to determine the first churn risk by applying at least theuser interaction data of the first user to a parameter functiongenerated based at least in part on a machine learning algorithm. 15.The system of claim 13, wherein the hardware processor is furtherconfigured to: predict a match outcome of a particular instance of thevideo game played by the first user and the second user based at leastin part on the user interaction data of the first user and the userinteraction data of the second user; and determine the first churn riskby applying at least the user interaction data of the first user and thepredicted match outcome to a parameter function generated based at leastin part on a machine learning algorithm.
 16. The system of claim 13,wherein the hardware processor is further configured to solve a minimumweight matching problem for the connected graph based at least in parton the weights assigned to each edge of the connected graph as part ofthe edge selection process.
 17. The system of claim 13, wherein thehardware processor is further configured to create the connected graphby at least: creating a fully connected graph with each vertexrepresenting a different user from the plurality of users; determining adesired video game match outcome for each user based at least in part onthe user interaction data for each user; and filtering the fullyconnected graph to obtain the connected graph based at least in part onthe desired video game match outcome for each user.
 18. A non-transitorycomputer-readable storage medium storing computer executableinstructions that, when executed by one or more computing devices,configure the one or more computing devices to perform operationscomprising: selecting a plurality of users from a pool of users who areavailable to play an instance of a video game, wherein at least a firstportion of the instance of the video game executes on a user computingdevice of at least one user from the plurality of users and a secondportion of the instance of the video game executes on the interactivecomputing system; creating a connected graph with each vertexrepresenting a different user from the plurality of users such that eachuser from the plurality of users is paired within the connected graphwith at least one other user from the plurality of users; accessing userinteraction data for each user of the plurality of users, the userinteraction data corresponding to the user's interaction with the videogame, wherein the user interaction data for the user is selected fromdata that is more recent than a particular time threshold; for each edgein the connected graph, assigning a weight to the edge based at least inpart on a first churn risk of a first user corresponding to a first nodeof the edge and a second churn risk of a second user corresponding to asecond node of the edge, wherein the first churn risk is based at leastin part on the user interaction data for the first user and the secondchurn risk is based at least in part on the user interaction data forthe second user; selecting a set of edges from the connected graph basedat least in part on the weights assigned to each edge within theconnected graph, wherein each vertex in the connected graph is connectedto one or more edges within the set of selected edges; and initiating aplayable instance of the video game using a plurality of userscorresponding to vertexes of one or more edges included in the set ofselected edges.
 19. The computer-readable, non-transitory storage mediumof claim 18, further comprising determining the first churn risk byapplying at least the user interaction data of the first user to aparameter function generated based at least in part on a machinelearning algorithm.
 20. The computer-readable, non-transitory storagemedium of claim 18, further comprising: predicting a match outcome of aparticular instance of the video game played by the first user and thesecond user based at least in part on the user interaction data of thefirst user and the user interaction data of the second user; anddetermining the first churn risk by applying at least the userinteraction data of the first user and the predicted match outcome to aparameter function generated based at least in part on a machinelearning algorithm.